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Abstract #3853

MRI-Enhanced Generative Model for Alzheimer's Disease: Converting 18F-FDG PET to 18F-AV-45 PET

Xing Fu1, Yuxi Jin1, Hongda Shao2, Haizhou Liu1, Yan Zhang2, Na Zhang3, Hairong Zheng1, Dong Liang1, Jianjun Liu2, and Zhanli Hu1
1Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3the Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen, China

Synopsis

Keywords: AI Diffusion Models, Multimodal

Motivation: The medical imaging diagnosis of Alzheimer's disease (AD) often relies on multi-modal images, yet obtaining these can be challenging due to high costs and short-lived tracers.

Goal(s): This research aims to address this limitation by developing a generative model that utilizes MRI to enhance the transformation from 18F-FDG PET to 18F-AV-45 PET images.

Approach: Using MRI as an enhancement modality, our approach enables faster acquisition of images to aid clinical diagnosis.

Results: The experimental results demonstrate the effectiveness and superiority of our method, offering a viable solution for more accessible and efficient AD diagnosis.

Impact: This method significantly enhances diagnostic efficiency in Alzheimer's disease by enabling quick, cost-effective image generation, reducing reliance on expensive, short-lived tracers, and providing accessible support for clinicians, ultimately advancing multi-modal medical imaging practices in neurodegenerative diseases.

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Keywords